1. Field of the Invention
The present invention relates to displaying magnetic resonance images and more particularly to linearly re-mapping the image intensity range of magnetic resonance images.
2. Description of the Prior Art
Magnetic Resonance (MR) images have typically a data depth of 12 bits. For displaying any 12-bit MR image on a common 8-bit computer monitor, the image intensity range of the MR image needs to be re-mapped in general. One of the common prior art re-mapping processes is the so-called windowing process. It maps the image intensity values linearly from [(center--width)/2, (center+width)/2] to [0, 255], where center and width are two display parameters to be adjusted. The image intensity values below [(center--width)/2] are mapped to zero, while they are set to 255 if greater than [(center+width)/2]. Obviously, these two parameters can greatly influence the appearance of the image displayed. In other words, the brightness and the contrast of an image is determined by these two parameters. Inadequate adjustment of these parameters can lead to degradation of image quality and in severe cases to loss of valuable diagnostic information of the images.
Automatic adjustment of the above-mentioned two display parameters is in fact a very complicated problem because of the following reasons:
The maximum and the minimum pixel intensity value are instable quantities for the automatic adjustment of display parameters. They can be influenced strongly by many factors, such as spikes and background noise. This makes any system that uses the maximum and minimum values of the image intensity instable.
The region of interest in the intensity domain may only occupy a small portion of the dynamic range of the image pixel values. It is therefore inadequate to map the entire dynamic range of an image to [0, 255].
The spatial distribution of an image may also play an important role in the adjustment of the display window parameters. Different organs may have similar intensity distribution (histograms) but should be windowed differently. This factor makes the use of other information sources beyond the histogram of MR images necessary. Unfortunately, all current algorithms known to us are based on histograms or its derivatives.
It is also evident that the optimal display parameters also depend on the type of MR examinations. Different types of MR images may need to be viewed, and hence windowed differently. For example, 3D angiographic images, T1 or T2 weighted images, and inversion recovery images should be windowed very differently.
In general, images acquired with body or surfaces coils have to be treated differently. Surface coil images tend to have much larger dynamic range than the body coil images.
Individuals may have very different preferences in terms of image brightness and contrast. Some testing results have shown significant differences in adjusting the image appearance among different users or user groups. There is also evidence indicating that large differences in adjusting the same image may even occur for the same individual at a different time.
The optimal display window parameter setting is environmental condition dependent. Different lighting conditions, for example, may influence the adjustment results greatly.
The optimal display parameters are also monitor dependent. Different monitor settings, such as the gamma curve, the brightness and the contrast control values, may also affect the window width and center parameters strongly. There may be even large difference between aged and new monitors.
There have been some prior art methods developed for the automatic adjustment of a display window's width and center values. The most basic method is to determine the maximal and the minimal intensity values of each MR image, and then map them to [0, 255] in a linear fashion. This method does not work well in many cases because it does not solve any of the problems mentioned above.
The weaknesses of this basic method can be overcome partially by a more robust histogram based method as proposed by W. J. Ryan, R. T. Avula, B. J. Erickson, J. P. Felmlee and N. J. Hanglandreou in "Automatic Window Width And Window Level Calculation For MR Images", Proceeding of Radiological Society of North America, pp.447, 1995. In this method, after the histogram of an image has been computed, all of the histogram bins with less than a certain number of pixels are removed for further calculation. This step is used to reduce the influence of bad pixels (outliers in the statistical sense), such as spikes and background noisy pixels. However, this method still can not provide satisfactory solutions for a wide range of MR images because it completely relies on the histogram information to determine the display parameters, which is not sufficient for the adjustment of the display window width and center for a wide variety of MR images. Through test data, many cases have been found where human operators have selected a very different setting of the display parameters for some images with very similar histograms, but with different spatial distributions. This problem will be discussed in more detail in the Detailed Description of the Invention. It is an object of the present invention to resolve this ambiguity for using histogram information only, by utilizing some spatial statistical information of the images in addition to the histogram information for the display window parameters determination. The performance and robustness of such a system would be greatly improved with the addition of spatial statistical information.
R. E. Wendt III in "Automatic Adjustment Of Contrast And Brightness Of Magnetic Resonance Images", Journal of Digital Imaging, Vol. 7, No. 2, pp. 95-97, 1994, has proposed a system which determines at first the type of an MR image by reading the image header information, and then computes the display parameters depending on the type of the image. This provides a possibility to determine the actual field of view of the image to avoid pixels lying on the edge and outside of the field of view. It also ignores pixels that have values below a threshold, depending on the type of image being processed. However, this system requires that different rules must be set for different types and orientations of MR images. This makes the system somewhat impractical, since the software may have to be re-programmed to reflect any new changes in the MR image acquiring process, such as the use of new coils or new pulse sequences. It is therefore very desirable to have some adaptation capability built-in so that the system can adapt to these new coils or sequences without the re-programming of the software. An obvious possibility is to use neural networks which provide "learn by example" capability. By showing the new examples, the neural networks can be re-trained without re-programming.
There is also a neural network based method developed for the automatic adjustment described by A. Ohhashi, S. Yamada, K. Haruki, H. Hatano, Y. Fujii, K. Yamaguchi and H. Ogata in "Automatic Adjustment Of Display Window For MR Images Using A Neural Network", Proceeding of SPIE, Vol. 1444, Image Capture, Formatting and Display, pp. 63-74, 1991. A group of six features from the gray-level histogram of the MR images to be displayed is at first extracted. A back-propagation neural network is then trained to learn the relationship between these features and the desired output of the optimal display parameters set by human operators. Although this method has some advantages by utilizing a neural network over the above two methods, it is still a pure histogram based method since the features for the training is generated only from the histogram. As mentioned above, pure histogram based methods will not be able to solve the potential problem for the images with different spatial distributions but very similar histograms. In addition, pure neural network based methods in general have problems when an input image type has not been trained before. Since MRI can generate a large amount of different image types with different pulse sequences and different TR, TE, TI parameters, it presents a great challenge for any pure neural network based method to cover all possible cases in the training phase. Furthermore, this method uses only one neural network to learn the relationship between histogram based features and the desired output of the optimal display parameters. However, in practice, one neural network in many cases is often not enough for capturing the very complex, sometimes even totally conflicting, relationship. There is an obvious need for using more networks to minimize the probability of conflicting situations. The problem becomes "how can multiple networks be organized and utilized in a meaningful way?" and "what kind of means can be used as a safety net if a totally unknown image is given to be windowed?".
It is an object of the present invention to address all of the issues mentioned above through the development of a comprehensive neural networks based system. Such a system, unlike other prior art systems, should be designed with easy extensions for on-line adaptation capability, thus it should be developed as an adaptive system.